Automated assessment reveals that the extinction risk of reptiles is widely underestimated across space and phylogeny

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning–based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles—the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.


Phylogenetic and spatial eigenvector maps
We will use the distributions and phylogeny to calculate eigenvector maps, which represent the spatial and phylogenetic autocorrelation in the data.

Training model
We use a hierarchical modeling framework, with the XGBoost algorithm separating species into specific threat categories. At each step, we perform hyperparameter tuning and feature selection. We also repeat the same procedure for four range size classes, based on IUCN B criterion (See Caetano et al, 2022 for more details).

Binary model
Our first step separates threatened from non-threatened species. There are several steps, including data processing, hyperparameter tuning and feature selection. Before proceeding with the model, we must do some data processing, which involves partitioning the data into training and testing data sets, creating dummy variables for categorical features, and converting the data to XGBoost format. We will create a function for this, so we don't have to repeat it every time.  list(dtrain = dtrain, dtest = dtest, response_tr = response_tr, response_ts = response_ts, new_tr = new_tr, new_ts = new_ts) } pred_skink_data <-lapply(pred_skink_list, data_process, cat = "threatened")

Hyperparameter tuning
Next we need to tune some hyperparameters in the model. These are modelling features such as learning rate, tree depth, weighting, sampling and regularization. See the XGBoost documentation for a description of parameters. We will create 10,000 random parameter samples and retain the one that leads to the highest AUC. There is a wealth of other hyperparameter tuning methods, we recommend researching into the alternatives so you can choose the most appropriate for your analysis.

Cross validation
You can see in the output above that the model already gives us a sample cross validation, but we will perform an out of sample validation as well. First, we predict categories for the test data:  We obtained a very high accuracy (92%) and high AUC (85%).

Specific categories
To train the model for specific categories, we repeat the steps above, but apply them to the subsets of data separated by the previous step, following the framework on Figure 1.

Predictions for DD and NE species
Now that we finished training our model, we can use it to predict the threat categories of DD and NE species. First, we must add an identification column, so we can later associate predictions with species.